{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.client.session.Session at 0x1f6baa889e8>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "from keras.applications.vgg16 import VGG16\n",
    "from keras.models import Model,Sequential\n",
    "from keras.layers.convolutional import Conv2D\n",
    "from keras.layers.recurrent import GRU\n",
    "from keras.layers.core import Reshape,Dense,Flatten,Permute,Lambda,Activation\n",
    "from keras.layers.wrappers import Bidirectional,TimeDistributed\n",
    "from keras.layers import Input\n",
    "from keras.optimizers import Adam,SGD\n",
    "from keras import backend as K\n",
    "from keras import regularizers \n",
    "import tensorflow as tf \n",
    "from keras.callbacks import EarlyStopping,ModelCheckpoint,Callback\n",
    "\n",
    "def rpn_loss_regr(y_true,y_pred):\n",
    "    \"\"\"\n",
    "    smooth L1 loss\n",
    "  \n",
    "    y_ture [1][HXWX9][3] (class,regr)\n",
    "    y_pred [1][HXWX9][2] (reger)\n",
    "    \"\"\"   \n",
    "    \n",
    "    sigma=9.0\n",
    "    \n",
    "    cls = y_true[0,:,0]\n",
    "    regr = y_true[0,:,1:3]\n",
    "    regr_keep = tf.where(K.equal(cls,1))[:,0]\n",
    "    regr_true = tf.gather(regr,regr_keep)\n",
    "    regr_pred = tf.gather(y_pred[0],regr_keep)\n",
    "    diff = tf.abs(regr_true-regr_pred)\n",
    "    less_one = tf.cast(tf.less(diff,1.0/sigma),'float32')\n",
    "    loss = less_one * 0.5 * diff**2 * sigma   + tf.abs(1-less_one) * (diff -0.5/sigma)\n",
    "    loss = K.sum(loss,axis=1)\n",
    "\n",
    "    return K.switch(tf.size(loss)>0,K.mean(loss),K.constant(0.0))\n",
    "\n",
    "def rpn_loss_cls(y_true,y_pred):\n",
    "    \"\"\"\n",
    "    softmax loss\n",
    "    \n",
    "    y_true [1][1][HXWX9] class\n",
    "    y_pred [1][HXWX9][2] class \n",
    "    \"\"\" \n",
    "    y_true = y_true[0][0]\n",
    "    cls_keep = tf.where(tf.not_equal(y_true,-1))[:,0]\n",
    "    cls_true = tf.gather(y_true,cls_keep)\n",
    "    cls_pred = tf.gather(y_pred[0],cls_keep)\n",
    "    cls_true = tf.cast(cls_true,'int64')\n",
    "    #loss = K.sparse_categorical_crossentropy(cls_true,cls_pred,from_logits=True)\n",
    "    loss = tf.nn.sparse_softmax_cross_entropy_with_logits(labels = cls_true,logits=cls_pred)\n",
    "    return K.switch(tf.size(loss)>0,K.clip(K.mean(loss),0,10),K.constant(0.0))\n",
    "\n",
    "\n",
    "def nn_base(input,trainable):\n",
    "    base_model = VGG16(weights=None,include_top=False,input_shape = input)\n",
    "    \n",
    "    return base_model.input,base_model.get_layer('block5_conv3').output\n",
    "\n",
    "\n",
    "def reshape(x):\n",
    "    b = tf.shape(x)\n",
    "    x = tf.reshape(x,[b[0]*b[1],b[2],b[3]])\n",
    "    return x\n",
    "\n",
    "def reshape2(x):\n",
    "    x1,x2 = x\n",
    "    b = tf.shape(x2)\n",
    "    x = tf.reshape(x1,[b[0],b[1],b[2],256])\n",
    "    return x \n",
    "\n",
    "def reshape3(x):\n",
    "    b = tf.shape(x)\n",
    "    x = tf.reshape(x,[b[0],b[1]*b[2]*10,2])\n",
    "    return x \n",
    "\n",
    "def rpn(base_layers):\n",
    "    \n",
    "    x = Conv2D(512,(3,3),strides=(1,1),padding='same',activation='relu',\n",
    "               name='rpn_conv1')(base_layers)\n",
    "    \n",
    "    x1 = Lambda(reshape,output_shape=(None,512))(x) \n",
    "    \n",
    "    x2 = Bidirectional(GRU(128,return_sequences=True),name='blstm')(x1)\n",
    "\n",
    "    x3 = Lambda(reshape2,output_shape=(None,None,256))([x2,x])\n",
    "    x3 = Conv2D(512,(1,1),padding='same',activation='relu',name='lstm_fc')(x3)\n",
    "\n",
    "    cls = Conv2D(10*2,(1,1),padding='same',activation='linear',name='rpn_class')(x3)\n",
    "    regr = Conv2D(10*2,(1,1),padding='same',activation='linear',name='rpn_regress')(x3)\n",
    "    \n",
    "\n",
    "    cls = Lambda(reshape3,output_shape=(None,2),name='rpn_class_reshape')(cls)\n",
    "    cls_prod = Activation('softmax',name='rpn_cls_softmax')(cls)\n",
    "\n",
    "    regr = Lambda(reshape3,output_shape=(None,2),name='rpn_regress_reshape')(regr)\n",
    "    \n",
    "    return cls,regr,cls_prod\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "import utils\n",
    "\n",
    "utils.get_session(gpu_fraction=0.8)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\n",
    "inp,nn = nn_base((None,None,3),trainable=False)\n",
    "cls,regr,cls_prod = rpn(nn)\n",
    "basemodel =  Model(inp,[cls,regr,cls_prod])\n",
    "basemodel.load_weights(r'E:\\deeplearn\\ctpn2018\\model\\weights-ctpnlstm-19.hdf5')\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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o90++j3XWWZfeffsk8tgHM2aw/nrrc8IJJ/D6669jjOE3xxzD5AfvRzkO8+fP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Mbw8MFDPJ90nl998SVN7dqRMOu/s2mh/ncgoGVZrQDUB7AdwBcA2pFcmO73oQCGAugC4AiAMQCq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\nU2PcABynxrgBOE6N+R8NZWzftZAecQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f9231c5e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import cv2\n",
    "import matplotlib.pyplot as plt \n",
    "import utils\n",
    "from text_proposal_connector_oriented import TextProposalConnectorOriented\n",
    "import numpy as np\n",
    "\n",
    "imgpath = r'E:\\deeplearn\\OCRonline\\chinese-ocr-demo\\test\\004.jpg'\n",
    "#precess img\n",
    "img = cv2.imread(imgpath)\n",
    "h,w,c = img.shape\n",
    "#zero-center by mean pixel \n",
    "m_img = img - utils.IMAGE_MEAN\n",
    "m_img = np.expand_dims(m_img,axis=0)\n",
    "\n",
    "cls,regr,cls_prod = basemodel.predict(m_img)\n",
    "\n",
    "\n",
    "anchor = utils.gen_anchor((int(h/16),int(w/16)),16)\n",
    "\n",
    "bbox = utils.bbox_transfor_inv(anchor,regr)\n",
    "bbox = utils.clip_box(bbox,[h,w])\n",
    "\n",
    "#score > 0.7\n",
    "fg = np.where(cls_prod[0,:,1]>0.7)[0]\n",
    "select_anchor = bbox[fg,:]\n",
    "select_score = cls_prod[0,fg,1]\n",
    "select_anchor = select_anchor.astype('int32')\n",
    "\n",
    "#filter size\n",
    "keep_index = utils.filter_bbox(select_anchor,16)\n",
    "\n",
    "\n",
    "#nsm\n",
    "select_anchor = select_anchor[keep_index]\n",
    "select_score = select_score[keep_index]\n",
    "select_score = np.reshape(select_score,(select_score.shape[0],1))\n",
    "nmsbox = np.hstack((select_anchor,select_score))\n",
    "keep = utils.nms(nmsbox,0.3)\n",
    "select_anchor = select_anchor[keep]\n",
    "select_score = select_score[keep]\n",
    "\n",
    "#text line\n",
    "textConn = TextProposalConnectorOriented()\n",
    "text = textConn.get_text_lines(select_anchor,select_score,[h,w])\n",
    "\n",
    "# for i in select_anchor:\n",
    "#         cv2.rectangle(img,(i[0],i[1]),(i[2],i[3]),(255,0,0),2)\n",
    "\n",
    "text= text.astype('int32')\n",
    "\n",
    "for i in text:\n",
    "    cv2.line(img,(i[0],i[1]),(i[2],i[3]),(255,0,0),2)\n",
    "    cv2.line(img,(i[0],i[1]),(i[4],i[5]),(255,0,0),2)\n",
    "    cv2.line(img,(i[6],i[7]),(i[2],i[3]),(255,0,0),2)\n",
    "    cv2.line(img,(i[4],i[5]),(i[6],i[7]),(255,0,0),2)\n",
    "\n",
    "plt.imshow(img)\n",
    "plt.show()\n",
    "cv2.imwrite(r'test\\110.jpg',img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(19, 9)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [default]",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
